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Cloudnet products available from Chilbolton

Cloudnet products available from Chilbolton. Robin Hogan Anthony Illingworth Ewan O’Connor Nicolas Gaussiat Malcolm Brooks University of Reading. Motivation. Clouds are crucial for weather & climate forecasting but their representation in models needs testing In this talk

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Cloudnet products available from Chilbolton

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  1. Cloudnet productsavailable from Chilbolton Robin Hogan Anthony Illingworth Ewan O’Connor Nicolas Gaussiat Malcolm Brooks University of Reading

  2. Motivation Clouds are crucial for weather & climate forecasting but their representation in models needs testing In this talk • Chilbolton cloud observations held by BADC • About the EU Cloudnet project • Radar and lidar basics • Instrument synergy/target categorization • Facilitates implementation of the algorithms • A few of the products and model comparisons • Target classification: ice/liquid, cloud/precipitation etc. • Cloud fraction • Ice water content

  3. Standard Chilbolton observations at BADC Radar Lidar, gauge, radiometers But can the average user make sense of these measurements?

  4. The EU CloudNet projectApril 2001 – April 2004 • Aim: to retrieve continuously the crucial cloud parameters for climate and forecast models • Three sites: Chilbolton (GB) Cabauw (NL) and Palaiseau (F) • To evaluate a number of operational models • Met Office (mesoscale and global versions) • ECMWF • Météo-France (Arpege) • KNMI (Racmo and Hirlam) • Crucial aspects • Report retrieval errors and data quality flags • Use common formats based around NetCDF allow all algorithms to be applied at all sites and compared to all models

  5. The three Cloudnet sites • Core instrumentation at each site • Radar, lidar, microwave radiometers, raingauge Cabauw, The Netherlands 1.2-GHz wind profiler + RASS (KNMI) 3.3-GHz FM-CW radar TARA (TUD) 35-GHz cloud radar (KNMI) 1064/532-nm lidar (RIVM) 905 nm lidar ceilometer (KNMI) 22-channel MICCY radiometer (Bonn) IR radiometer (KNMI) SIRTA, Palaiseau (Paris), France 5-GHz Doppler Radar (Ronsard) 94-GHz Doppler Radar (Rasta) 1064/532 nm polarimetric lidar 10.6 µm Scanning Doppler Lidar 24/37-GHz radiometer (DRAKKAR) 23.8/31.7-GHz radiometer (RESCOM) Chilbolton, UK 3-GHz Doppler/polarisation radar (CAMRa) 94-GHz Doppler cloud radar (Galileo) 35-GHz Doppler cloud radar (Copernicus) 905-nm lidar ceilometer 355-nm UV lidar 22.2/28.8 GHz dual frequency radiometer

  6. Basics of radar and lidar Penetrates ice cloud Detects cloud top Strong echo from liquid clouds Detects cloud base Radar: Z~D6 Sensitive to large particles (ice, drizzle) Lidar: b~D2 Sensitive to small particles (droplets, aerosol) Radar/lidar ratio provides information on particle size

  7. Cloudnet processing chain

  8. The Instrument synergy/Target categorization product • Makes multi-sensor data much easier to use: • Combines radar, lidar, model, raingauge and -wave radiometer • Identical format for each site • Performs many common pre-processing tasks: • Interpolation on to the same grid • Ingest model data (many algorithms need temperature & wind) • Correction of radar for gaseous attenuation (using model humidity) and liquid attenuation (using m-wave LWP and lidar) • Quantify random and systematic measurement errors • Quantify instrument sensitivity • Categorization of atmospheric targets: does my algorithm work with this target/hydrometeor type? • Data quality: are the data reliable enough for my algorithm?

  9. Target categorization • Combining radar, lidar and model allows the type of cloud (or other target) to be identified • From this can calculate cloud fraction in each model gridbox

  10. Ice water content from reflectivity and temperature • Error in ice water content • Retrieval flag Mostly retrieval error Mostly liquid attenuation correction error

  11. Cloud fraction Observations Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model KNMI Regional Atmospheric Climate Model

  12. Ice water Observations Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model KNMI Regional Atmospheric Climate Model

  13. Comparison of mean cloud fraction and ice water content • One year of data from Chilbolton

  14. IWC distributions High cloud Observations Unified Model Mid-level • The Met Office Unified Model tends to simulate very high and very low ice water contents too infrequently

  15. Cloud fraction skill score • Model performance: • ECMWF, RACMO, Met Office models perform similarly • Météo France not so well, much worse before April 2003 • Met Office model significantly better for shorter lead time

  16. Other Cloudnet products • Radar/lidar ice water content and particle size • KNMI algorithm: restricted to clouds penetrated by lidar, but more accurate than IWC from radar alone • Radar/lidar drizzle flux and drizzle drop size • Important for lifetime of stratocumulus in climate models • Cloud phase (part of target categorization product) • Important for cloud radiative properties: details later today • Turbulent dissipation rate, dual-wavelength radar liquid water content and ice products • Details later today Visit our web site at www.met.rdg.ac.uk/radar/cloudnet

  17. Cloud fraction Model gridboxes • Radar provides first guess of cloud fraction in each model gridbox Lidar refines the estimate by removing drizzle beneath stratocumulus and adding thin liquid clouds (warm and supercooled) that the radar does not detect

  18. Ice water content from cloud radar • Cirrus in situ measurements suggest we can obtain IWC from Z and temperature to to a factor of two -30%/+40% Met Office aircraft data IWC also available from KNMI radar/lidar algorithm

  19. Contingency tables Model cloud Model clear-sky Comparison with Met Office model over Chilbolton, October 2003 Observed cloud Observed clear-sky

  20. Skill versus time • Cabauw Equitable threat score • Cabauw mean cloud fraction • Chilbolton Equitable threat score • Chilbolton mean cloud fraction Change in Météo France cloud scheme April 2003

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